Date Published: October 16, 2015
Publisher: Public Library of Science
Author(s): Lindsay P. Campbell, Andrew O. Finley, M. Eric Benbow, Jenni Gronseth, Pamela Small, Roch Christian Johnson, Ghislain E. Sopoh, Richard M. Merritt, Heather Williamson, Jiaguo Qi, Xiao-Nong Zhou. http://doi.org/10.1371/journal.pntd.0004123
Abstract: BackgroundLand use and land cover (LULC) change is one anthropogenic disturbance linked to infectious disease emergence. Current research has focused largely on wildlife and vector-borne zoonotic diseases, neglecting to investigate landscape disturbance and environmental bacterial infections. One example is Buruli ulcer (BU) disease, a necrotizing skin disease caused by the environmental pathogen Mycobacterium ulcerans (MU). Empirical and anecdotal observations have linked BU incidence to landscape disturbance, but potential relationships have not been quantified as they relate to land cover configurations.Methodology/Principal FindingsA landscape ecological approach utilizing Bayesian hierarchical models with spatial random effects was used to test study hypotheses that land cover configurations indicative of anthropogenic disturbance were related to Buruli ulcer (BU) disease in southern Benin, and that a spatial structure existed for drivers of BU case distribution in the region. A final objective was to generate a continuous, risk map across the study region. Results suggested that villages surrounded by naturally shaped, or undisturbed rather than disturbed, wetland patches at a distance within 1200m were at a higher risk for BU, and study outcomes supported the hypothesis that a spatial structure exists for the drivers behind BU risk in the region. The risk surface corresponded to known BU endemicity in Benin and identified moderate risk areas within the boundary of Togo.Conclusions/SignificanceThis study was a first attempt to link land cover configurations representative of anthropogenic disturbances to BU prevalence. Study results identified several significant variables, including the presence of natural wetland areas, warranting future investigations into these factors at additional spatial and temporal scales. A major contribution of this study included the incorporation of a spatial modeling component that predicted BU rates to new locations without strong knowledge of environmental factors contributing to disease distribution.
Partial Text: Land use and land cover (LULC) change at multiple spatial and temporal scales is one anthropogenic disturbance linked to infectious disease emergence . Anthropogenic activities with major impacts on LULC are land degradation, including agriculture intensification and water projects, urbanization, and deforestation . These activities can lead to ecological edge effects that promote disease emergence . Further, these activities generate new pathways through which humans can interact with previously undisturbed environments, resulting in closer proximities to potential vectors, reservoirs, and isolated pathogens [3–6].
The southern portions of Benin and Togo, West Africa comprise our study area, 6.30°N—8.17°N and 0.84°E to 2.48°E (Fig 1). Four major rivers flow through the study area, including the Couffu, Ouémé and Zou rivers in Benin, and the Mono River that delineates the southern border between Togo and Benin. BU is endemic in Benin and Togo, but incidence data from Togo were incomplete. Therefore, this study used BU case observations and corresponding environmental data in Benin to identify significant drivers, and then predicts BU risk across southern Benin and Togo.
The majority of BU positive communities in Benin were located in the southern region of the country (Fig 2). The departments of Zou and Couffo had the highest number of BU positive communities in our data set, with 51 and 59 locations. Ouémé, Mono, and Atlantique followed with 38, 19, and 14 BU positive communities, with Plateau and Collines having 8 and 4 positive locations from our sample data set.
This study is the first to investigate land cover configurations indicative of anthropogenic disturbances in relation to BU incidence. Model results provided insight into BU incidence in West Africa, while demonstrating the value of spatial modeling approaches in disease ecology investigations. Lower DIC values corresponding to spatial models support the hypothesis that spatial structure exists for drivers of BU incidence in the region. Importantly, comparisons between non-spatial and spatial variable significance demonstrated the potential for inaccurate estimates to occur when using non-spatial models to address ecological problems. The inclusion of the spatial random effects accounted for missing predictor variables and provided substantial improvements in predictions of BU rates at unsampled locations (Table 2).